Artificial Intelligence-Based Stationkeeping for Drillships Under Irregular Sea States

As near-shore oil reserves deplete, floating offshore platforms - particularly drillships - have become critical for ultra-deep-water oil and gas exploration. Maintaining precise positioning under dynamic sea conditions is vital to prevent operational failures. This study introduces an AI-integrated station-keeping framework for drillships operating under irregular sea states. A high-fidelity numerical model of a reference drillship is developed in ANSYS Explicit Dynamics to simulate dynamic responses (surge, sway, yaw) across multiple sea states based on the JONSWAP wave spectrum. The response data are used to train a MATLAB supervised regression-based machine learning model, which predicts optimal corrective thrust based on real-time positional deviations. Simulation results show an 80% reduction in surge displacement compared to uncontrolled motion. Integrating interpretable AI into the Dynamic Positioning System (DPS) demonstrates enhanced adaptability, reduced reliance on satellite navigation, and improved station-keeping performance. This approach presents a scalable pathway toward resilient, data-driven control for offshore drilling operations.